Humanity’s Great AI Race: New Players are Competing to Invent the Future of Healthcare

The Great AI Race

There is a Great AI Race underway – three of them actually – that will profoundly impact the future of civilization; yet, few people beyond the competitors and supporters even know it exists. The prizes to winners will be the redistribution of as much as $1.74 trillion dollars per year – the value of the global pharmaceutical market plus the amount spent on preventable medical care. And, for those of us who hope that our lives shall be well-lived, and fear that they shall not, the prize is the certainty that our existence was meaningful by saving millions of lives every year.

Near his life’s end, Steve Jobs, the founder and CEO of Apple Computers, predicted a new era was beginning, one that would impact history more than the computer chips invention. He predicted this new era would be at the intersection of technology and biology and reshape civilization. It has begun. And, while there will be tens of thousands of ways this will be true, most fall into the category of the Great Race to transform medicine from reactive to proactive and predictive.

By now, Steve Jobs is not alone in his prediction. Global consulting leader McKinsey & Company has called AI the “road to digital success in pharma” and estimated it could increase profits by $100 billion per year (D. Champagne, 2015). Forbes magazine has called the intersection “amazing.” Fortune magazine has called it “a digital healthcare revolution”(Mukherjee, 2017). The Wall Street Journal characterized the impact of AI on healthcare and pharmaceuticals as “transformative” (Guisbond, 2016). Preeminent venture-capital firm Andreessen Horowitz has called it “the third phase of medicine” (Farr, 2017). Harvard Business Review has written that AI and machine learning are “the most important general-purpose innovations of our era” (Erik Brynjolfsson, 2017). And, no less an authority than the MIT Technology Review has described digital pharma as the “Holy Grail of Silicon Valley” (Farr, 2017).

In the last 90 days of 2017, I traveled over 100,000 miles throughout Asia witnessing over 100 companies from all over the world pitching how they plan to compete in this Great Race. They were acclaimed researchers, award-winning start-ups, and leaders in global corporations. And, before that, I traveled and spoke at leading conferences focused on AI in healthcare, completed an advanced degree in AI applied to health informatics at a leading university, and met with dozens of executives from the world’s leading healthcare companies. This is what I learned.

Three Races in One

Arguably, there are three races occurring in parallel: the first is in adding value to own market share; the second is in data and talent aggregation; and, the third is in creating intellectual property to build patent portfolios of exclusivity. The value-add race is largely about creating integrated-care technologies initially, then software or apps called digital therapeutics seeking to improve or replace chemical medicines, then machine-learning diagnostics to predict and prevent disease. The data aggregation race accurately presumes that the firms with the highest quality causal data – note, not corollary – will have the raw materials to best employ AI to solve and predict medical problems. In some cases, technology companies wanting to become healthcare leaders will develop and deploy devices at low or no cost simply to own the data streams. The intellectual property (IP) race is controversial, largely because very large firms that struggle with executing innovation or attracting the best talent, typically file hundreds or even thousands of patent applications to own a monopoly on the licensing rights for 20 or more years, without executing, or not executing well, on the technology. This is sometimes viewed as disingenuous and captured with the derogatory term “patent trolls.”

The Winning Ingredients & Competitive Advantages

So, what will historic competitors, these new technology competitors, and entrepreneurs need to successfully compete in these Great Races? Essentially, three things: (1) talent; (2) data; and, (3) resources – usually money. By sheer number, the volume of entrepreneurs is the largest cohort of competitors, undoubtedly fueled by the inspiring social construct of capitalism – that anyone can invent the future and pull themselves up by their bootstraps if they have the best idea and work hard. According to Forbes, $3.5 billion was invested into digital-health startups in the first six months of 2017 alone (Goyal, 2017). However, entrepreneurs and start-ups tend to be strong on talent but have little to no data or resources. Therefore, they are superb at innovating but find it difficult to execute to grow that innovation into market leadership. To the degree that entrepreneurs have some success, larger competitors often buy them because they can grow faster and more effectively by buying and building.

The technology companies competing in this space – Amazon, Google (Verily, Brain & DeepMind), Apple, Samsung, Tencent, GE, etc. – are better positioned to prevail than most startups because they already have data and resources and can typically buy the talent – data scientists with cross-functional expertise in AI, statistics, and subject-matter expertise (e.g., health informatics).

However, my vote for the best-placed to win is pharmaceutical companies because while, like their technological competitors, they have data and resources and may have to buy talent, they have foundations in healthcare that the tech companies do not. Namely, pharmas have people and processes in place to secure critical Food & Drug Administration (FDA) approvals to validate prescriptive apps, accelerating their adoption. Pharmas also have a trusted distribution system in place via channels and relationships with millions of providers, and sales teams to explain the technology to those providers.

Data & Talent Wars

Of the three elements necessary to win these races applying AI to healthcare, talent is the scarcest. It’s scarcest because it requires a confluence of three skill sets – whether from education, experience, or both – that are scarce in and of themselves: (a) technology development in artificial intelligence; (b) statistics and advanced mathematics; and, (c) subject matter expertise in healthcare, ideally health informatics.

The fact that all three types of industry competitors – start-ups, technology companies, and pharmaceutical manufacturers – are competing for an already scarce group of talent also has a compounding effect – increasing demand with a fixed low supply – dramatically increasing prices. For example, Fortune magazine predicts that companies will spend $650 million this year hiring AI talent (Jones, 2017). For example, Google Research – their research and development arm for future technologies – is recruiting data scientists with compensation packages of $175,000 in salary (plus or minus $25,000), $50,000 signing bonuses and $100,000 per year in stock that vests over three years, with substantial annual increases thereafter in salaries and stock grants. As another market-based example, a search today on the employment meta-site Indeed revealed 740 open jobs in machine learning paying more than $100,000 – just in the city of Boston – and 97 open jobs paying over $150,000 in base salary.

So, where and how is this scarce AI talent distributed today? According to a survey of global AI talent administered by LinkedIn, the largest number, by a factor of five, are working in the United States, with India and Great Britain nearly tying for a distant second and third, but 44% of the influx of AI talent into the United States, is coming from China. A little less than half of the US AI talent’s education is limited to a bachelor’s degree, and only 14% of all the AI talent in the U.S. is in Boston, the epicenter of global pharma. Moreover, not a single pharmaceutical company is in the top 10 employers of AI talent in the United States – Microsoft is 1st, Google is 2nd, Amazon is 3rd, IBM is 4th, Apple is 5th, Facebook is 6th, Intel is 7th, Oracle is 8th, Bank of America is 9th, and Wells Fargo is 10th (Hersey, 2017).

The data aggregation wars have taken a different tact – namely to create pipelines for the ingestion of large quantities of health informatics data from providers via electronic health records and sensors and devices for patient-generated data. They are creating and owning data pipelines via the medical Internet of Medical Things (mIoT). Markets and markets, a technology industry research firm, has estimated the value of the medical Internet of Things market at $41.22 billion in 2017, and that it will grow at a compound annual growth rate (CAGR) of 30.8% making it worth $158.07 billion by 2022 (Markets and Markets, 2017). Further to the point here, Statista estimates there are 500 million smart wearables circulating in 2017 capable of transmitting health data, and that number will increase to 848 million cumulative devices in 2018 and 1.4 billion cumulative devices by 2019, each capable of streaming health data for storage and analysis to one of these competitors (Bresnick, 2017).

The IP Race

According to researcher Alexej Gossman, a Ph.D. student at Tulane University, 6,665 patent applications were filed in the United States relating to machine learning in the first 8.5 months of 2017 alone. While it’s impossible to tell what portion of these AI applications are specific to healthcare, they may not need to be as once an algorithm is created and patented, it can theoretically be applied to any type of data.

The identity of the applicants and grantees is telling. The top five are technology companies – IBM at 600, Google at 250, Amazon at 180, Microsoft at 175, and Samsung at 170. Apple, who two years ago hired 86 AI experts to make its talent pool competitive, is currently a distant 13th; however, not a single pharmaceutical company is in the top 20 (Duhaime-Ross, 2015) list of applicants for AI patents. The importance that this imputes is that while pharmaceutical companies may be best positioned to win the races – because of their data, resources, talent acquisition capabilities, FDA-approval engines, and trusted distribution channels with providers – pharmas are way behind in AI and need to dramatically accelerate their pace of innovation (Gossmann, 2017).

The Value-Add Race

How are these new and old players racing to disrupt health care? Generally, their research, innovations, and investments fall into one of three functional categories, to: (1) discover; (2) disintermediate; or, (3) predict to prevent.

In the discovery category of competitors, existing pharmaceutical companies are forming substantial financial relationships with technology companies to try to dramatically reduce and accelerate the $2.6 billion and 10-year time horizon for new drug treatments by as much as 25%. Pfizer has partnered with IBM Watson, Roche with GNS Healthcare, Sanofi with Exscientia, and GSK also with Exscientia and Insilico Medical in a deal worth an estimated $50M simply for exploration, Johnson & Johnson with BenevolentAI, and numerous pharmaceutical companies with XtalPi. One interesting thing about this trend is that not only are major new players entering the race but that some of those new players – like XtalPi – are backed by other new players with enormous resources to compete. For example, XtalPi is backed by both Sequoia Capital, one of the world’s largest and most successful venture-capital firms, and Chinese mega-company Tencent, itself with over $151 billion in annual revenues.

The second category of competitors though has an even greater potential to disrupt the existing pharmaceutical and healthcare industry. These companies are often called digital pharmaceuticals, (or “digiceuticals”) a term of art invented by Vijay Pande, a former MIT professor, who is now the lead healthcare investing partner at Andreessen Horowitz, arguably the most preeminent venture capital firm in the world. The modus operandi of these companies is to use AI to monitor and predict behaviors in real time with a goal of coaching patients to modify their lifestyles to forgo or prevent the need for treatment with chemical medications. Simply put, they are attempting to replace pills with apps to intervene upstream from when chemical medications are prescribed to thwart their need and steal pharmaceutical manufacturers’ market share. They literally aim to put pharmaceutical manufacturers largely out of business in certain treatment areas. Chronic public health ailments that are susceptible to this type of disintermediation include diabetes, coronary heart or vascular disease, and pulmonary disorders.

It’s the third category of competitors in this value-add Great Race that will arguably determine the winners of the final stages by applying AI to predict and prevent. Analyzing patient behaviors from sensors and patient-generated data is a start in this direction; however, it’s the ability to apply unsupervised machine learning to genomic and other -omics data (e.g., epigenetics, proteomics, microbiomes, etc.) that will have the greatest transformation of the pharmaceutical and healthcare industries. These machine-learning applications are designed to automatically screen for the biomarker “fingerprint” of all known diseases across a patient’s genome to predict who will develop which disease in the future – often years before clinical symptoms appear. This is intended to enable preventative treatments, including CRISPR gene editing, synthetic biology, and other prophylactic interventions that forgo the need to ever be treated with chemical medications because diseases never develop.

Adapt or Die

Charles Darwin is often misquoted as “survival of the fittest;” however, what Darwin concluded was species that survive are those “most adaptable to change.” The modern, digital, AI application of this is the now classic film “Moneyball,” about how one data scientist on one team changed the way the sport is managed and played. It created the free-market equivalency of “table stakes” in poker. Once one company uses AI to revolutionize the amount of intelligent value they could add, all competitors must do the same or face extinction because they are unable to continue in the game.

One of the things that’s most interesting about these Great Races is the thoroughness and speed of their ability to displace not just an entire industry, but one of the largest industries on Earth. Over the next 10-20 years, unless pharmaceutical companies embrace AI and catch-up to the technology companies seeking to replace them, pharma companies could lose 30-80% of their market share and capitalizations.

Bill Gates is credited with predicting that, someday, Microsoft will be put out of business. And, that this will happen because they didn’t see a competitor coming or imagine the existential threat and need to adapt to new business models, new products and services, for new clients. Today, the life-changing contributions made by the pharmaceutical industry in the last century are under siege by technology companies attempting to usurp their reason for existing. And, while these pharmaceutical companies are best positioned to win the Great Races, they are way behind and need to work faster and do more to catch up and win.

Eric Luellen the CEO of Bioinformatix an award-winning innovator at the confluence of data science, AI & machine learning, healthcare, and genomics with over 15 years’ industry experience developing high-return complex software solutions with big data and growing technology innovation start-ups. Mr. Luellen was a finalist for three global honors in health-informatics innovation in the last two years.